12 papers
Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing
Xiaolong Xu, Hao Jiang, Bo Jiang +5
Diffusion large language models (dLLMs) are appealing for code generation because they can update many tokens in parallel, yet many are optimized for sequence synthesis rather than…
Reference-Sampled Boltzmann Projection for KL-Regularized RLVR: Target-Matched Weighted SFT, Finite One-Shot Gaps, and Policy Mirror Descent
Yao Shu, Chenxing Wei, Hongbin Lin +2
Online reinforcement learning with verifiable rewards (RLVR) turns checkable outcomes into a scalable training signal, but it keeps rollout generation, verifier scoring, and refere…
MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games
Yunfei Xie, Kevin Wang, Bobby Cheng +9
Multi-turn, multi-agent LLM game evaluations often exhibit substantial run-to-run variance. In long-horizon interactions, small early deviations compound across turns and are ampli…
SAGE: Multi-Agent Self-Evolution for LLM Reasoning
Yulin Peng, Xinxin Zhu, Chenxing Wei +4
Reinforcement learning with verifiable rewards improves reasoning in large language models (LLMs), but many methods still rely on large human-labeled datasets. While self-play redu…
LFPO: Likelihood-Free Policy Optimization for Masked Diffusion Models
Chenxing Wei, Jiazhen Kang, Hong Wang +8
Reinforcement Learning with Verifiable Rewards (RLVR) has achieved remarkable success in improving autoregressive models, especially in domains requiring correctness like mathemati…
Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation
Chenxing Wei, Hong Wang, Ying He +4
Test-time policy adaptation for multi-turn interactions (T2PAM) is essential for aligning Large Language Models (LLMs) with dynamic user needs during inference time. However, exist…
Scheduling Your LLM Reinforcement Learning with Reasoning Trees
Hong Wang, Zhezheng Hao, Jian Luo +6
Using Reinforcement Learning with Verifiable Rewards (RLVR) to optimize Large Language Models (LLMs) can be conceptualized as progressively editing a query's `Reasoning Tree'. This…
GAPO: Robust Advantage Estimation for Real-World Code LLMs
Jianqing Zhang, Zhezheng Hao, Wei Xia +7
Reinforcement learning (RL) is widely used for post-training large language models (LLMs) in code editing, where group-relative methods, such as GRPO, are popular due to their crit…
Test-Time Policy Adaptation for Enhanced Multi-Turn Interactions with LLMs
Chenxing Wei, Hong Wang, Ying He +2
Large Language Models (LLMs) employ multi-turn interaction as a fundamental paradigm for completing complex tasks. However, their performance often degrades in extended interaction…
UniSVG: A Unified Dataset for Vector Graphic Understanding and Generation with Multimodal Large Language Models
Jinke Li, Jiarui Yu, Chenxing Wei +5
Unlike bitmap images, scalable vector graphics (SVG) maintain quality when scaled, frequently employed in computer vision and artistic design in the representation of SVG code. In…
ReDit: Reward Dithering for Improved LLM Policy Optimization
Chenxing Wei, Jiarui Yu, Ying Tiffany He +3
DeepSeek-R1 has successfully enhanced Large Language Model (LLM) reasoning capabilities through its rule-based reward system. While it's a ''perfect'' reward system that effectivel…
Flexora: Flexible Low Rank Adaptation for Large Language Models
Chenxing Wei, Yao Shu, Ying Tiffany He +1
Large Language Models (LLMs) are driving advancements in artificial intelligence by increasing the scale of model parameters, which has significantly enhanced generalization abilit…